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Relevant bibliographies by topics / Classification/segmentation

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Academic literature on the topic 'Classification/segmentation'

Author: Grafiati

Published: 1 June 2024

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Journal articles on the topic "Classification/segmentation"

1

Levner, Ilya, and Hong Zhang. "Classification-Driven Watershed Segmentation." IEEE Transactions on Image Processing 16, no. 5 (May 2007): 1437–45. http://dx.doi.org/10.1109/tip.2007.894239.

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Pavlidis, Theo, and Jiangying Zhou. "Page segmentation and classification." CVGIP: Graphical Models and Image Processing 54, no. 6 (November 1992): 484–96. http://dx.doi.org/10.1016/1049-9652(92)90068-9.

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Khanykov, I. G. "Classification of image segmentation algorithms." Izvestiâ vysših učebnyh zavedenij. Priborostroenie 61, no. 11 (November 30, 2018): 978–87. http://dx.doi.org/10.17586/0021-3454-2018-61-11-978-987.

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Wang, Ying, Jie Su, Qiuyu Xu, and Yixin Zhong. "A Collaborative Learning Model for Skin Lesion Segmentation and Classification." Diagnostics 13, no. 5 (February 28, 2023): 912. http://dx.doi.org/10.3390/diagnostics13050912.

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The automatic segmentation and classification of skin lesions are two essential tasks in computer-aided skin cancer diagnosis. Segmentation aims to detect the location and boundary of the skin lesion area, while classification is used to evaluate the type of skin lesion. The location and contour information of lesions provided by segmentation is essential for the classification of skin lesions, while the skin disease classification helps generate target localization maps to assist the segmentation task. Although the segmentation and classification are studied independently in most cases, we find meaningful information can be explored using the correlation of dermatological segmentation and classification tasks, especially when the sample data are insufficient. In this paper, we propose a collaborative learning deep convolutional neural networks (CL-DCNN) model based on the teacher–student learning method for dermatological segmentation and classification. To generate high-quality pseudo-labels, we provide a self-training method. The segmentation network is selectively retrained through classification network screening pseudo-labels. Specially, we obtain high-quality pseudo-labels for the segmentation network by providing a reliability measure method. We also employ class activation maps to improve the location ability of the segmentation network. Furthermore, we provide the lesion contour information by using the lesion segmentation masks to improve the recognition ability of the classification network. Experiments are carried on the ISIC 2017 and ISIC Archive datasets. The CL-DCNN model achieved a Jaccard of 79.1% on the skin lesion segmentation task and an average AUC of 93.7% on the skin disease classification task, which is superior to the advanced skin lesion segmentation methods and classification methods.

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Sekhar, Mr Ch, Ms A. Sharmila, Mr Ch Narayana, Mr A. Rutwick, Mr B. Srinu, Mr D. Pramod Kumar, and Mr B. Snehith. "Osteoporosis Diagnosis through Visual Segmentation and Classification: Extensive Review." International Journal of Research Publication and Reviews 5, no. 3 (March 9, 2024): 3748–53. http://dx.doi.org/10.55248/gengpi.5.0324.0771.

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Hyun-Cheol Park, Hyun-Cheol Park, Raman Ghimire Hyun-Cheol Park, Sahadev Poudel Raman Ghimire, and Sang-Woong Lee Sahadev Poudel. "Deep Learning for Joint Classification and Segmentation of Histopathology Image." 網際網路技術學刊 23, no. 4 (July 2022): 903–10. http://dx.doi.org/10.53106/160792642022072304025.

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<p>Liver cancer is one of the most prevalent cancer deaths worldwide. Thus, early detection and diagnosis of possible liver cancer help in reducing cancer death. Histopathological Image Analysis (HIA) used to be carried out traditionally, but these are time-consuming and require expert knowledge. We propose a patch-based deep learning method for liver cell classification and segmentation. In this work, a two-step approach for the classification and segmentation of whole-slide image (WSI) is proposed. Since WSIs are too large to be fed into convolutional neural networks (CNN) directly, we first extract patches from them. The patches are fed into a modified version of U-Net with its equivalent mask for precise segmentation. In classification tasks, the WSIs are scaled 4 times, 16 times, and 64 times respectively. Patches extracted from each scale are then fed into the convolutional network with its corresponding label. During inference, we perform majority voting on the result obtained from the convolutional network. The proposed method has demonstrated better results in both classification and segmentation of liver cancer cells.</p> <p> </p>

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Pandeya, Yagya Raj, Bhuwan Bhattarai, and Joonwhoan Lee. "Tracking the Rhythm: Pansori Rhythm Segmentation and Classification Methods and Datasets." Applied Sciences 12, no. 19 (September 23, 2022): 9571. http://dx.doi.org/10.3390/app12199571.

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This paper presents two methods to understand the rhythmic patterns of the voice in Korean traditional music called Pansori. We used semantic segmentation and classification-based structural analysis methods to segment the seven rhythmic categories of Pansori. We propose two datasets; one is for rhythm classification and one is for segmentation. Two classification and two segmentation neural networks are trained and tested in an end-to-end manner. The standard HR network and DeepLabV3+ network are used for rhythm segmentation. A modified HR network and a novel GlocalMuseNet are used for the classification of music rhythm. The GlocalMuseNet outperforms the HR network for Pansori rhythm classification. A novel segmentation model (a modified HR network) is proposed for Pansori rhythm segmentation. The results show that the DeepLabV3+ network is superior to the HR network. The classifier networks are used for time-varying rhythm classification that behaves as the segmentation using overlapping window frames in a spectral representation of audio. Semantic segmentation using the DeepLabV3+ and the HR network shows better results than the classification-based structural analysis methods used in this work; however, the annotation process is relatively time-consuming and costly.

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Vohra, Sumit K., and Dimiter Prodanov. "The Active Segmentation Platform for Microscopic Image Classification and Segmentation." Brain Sciences 11, no. 12 (December 14, 2021): 1645. http://dx.doi.org/10.3390/brainsci11121645.

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Image segmentation still represents an active area of research since no universal solution can be identified. Traditional image segmentation algorithms are problem-specific and limited in scope. On the other hand, machine learning offers an alternative paradigm where predefined features are combined into different classifiers, providing pixel-level classification and segmentation. However, machine learning only can not address the question as to which features are appropriate for a certain classification problem. The article presents an automated image segmentation and classification platform, called Active Segmentation, which is based on ImageJ. The platform integrates expert domain knowledge, providing partial ground truth, with geometrical feature extraction based on multi-scale signal processing combined with machine learning. The approach in image segmentation is exemplified on the ISBI 2012 image segmentation challenge data set. As a second application we demonstrate whole image classification functionality based on the same principles. The approach is exemplified using the HeLa and HEp-2 data sets. Obtained results indicate that feature space enrichment properly balanced with feature selection functionality can achieve performance comparable to deep learning approaches. In summary, differential geometry can substantially improve the outcome of machine learning since it can enrich the underlying feature space with new geometrical invariant objects.

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Abbas, Khamael, and Mustafa Rydh. "Satellite Image Classification and Segmentation by Using JSEG Segmentation Algorithm." International Journal of Image, Graphics and Signal Processing 4, no. 10 (September 17, 2012): 48–53. http://dx.doi.org/10.5815/ijigsp.2012.10.07.

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Mittal, Praveen, and Charul Bhatnagar. "Detection of DME by Classification and Segmentation Using OCT Images." Webology 19, no. 1 (January 20, 2022): 601–12. http://dx.doi.org/10.14704/web/v19i1/web19043.

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Optical Coherence Tomography (OCT) is a developing medical scanning technique proposing non- protruding scanning with high resolution for biological tissues. It is extensively employed in optics to accomplish investigative scanning of the eye, especially the retinal layers. Various medical research works are conducted to evaluate the usage of Optical Coherence Tomography to detect diseases like DME. The current study provides an innovative, completely automated algorithm for disease detection such as DME through OCT scanning. We performed the classification and segmentation for the detection of DME. The algorithm used employed HOG descriptors as feature vectors for SVM based classifier. Cross-validation was performed on the SD-OCT data sets comprised of volumetric images obtained from 20 people. Out of 10 were normal, while 10 were patients of diabetic macular edema (DME). Our classifier effectively detected 100% of cases of DME while about 70% cases of healthy individuals. The development of such a notable technique is extremely important for detecting retinal diseases such as DME.

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Dissertations / Theses on the topic "Classification/segmentation"

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Porter, Robert Mark Stefan. "Texture classification and segmentation." Thesis, University of Bristol, 1997. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.389032.

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Tan, Tieniu. "Image texture analysis : classification and segmentation." Thesis, Imperial College London, 1990. http://hdl.handle.net/10044/1/8697.

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Wong, Jennifer L. "A material segmentation and classification system." Thesis, Massachusetts Institute of Technology, 2013. http://hdl.handle.net/1721.1/85523.

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Thesis: M. Eng., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2013.
Cataloged from PDF version of thesis.
Includes bibliographical references (page 75).
In this thesis, I developed a material segmentation and classification system that takes in images of an object and identifies the material composition of the object's surface. The 3D surface is first segmented into regions that likely contain the same material, using color as a heuristic measure. The material classification of each region is then based on the cosine lobe model. The cosine lobe model is our adopted reflectance model, which allows for a simple approximation of a material's reflectance properties, which then serves as the material's unique signature.
by Jennifer L. Wong.
M. Eng.

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Anusha, Anusha. "Word Segmentation for Classification of Text." Thesis, Uppsala universitet, Institutionen för informationsteknologi, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-396969.

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Compounding is a highly productive word-formation process in some languages that is often problematic for natural language processing applications. Word segmentation is the problem of splitting a string of written language into its component words. The purpose of this research is to do a comparative study on different techniques of word segmentation and to identify the best technique that would aid in the extraction of keyword from the text. English was chosen as the language. Dictionary-based and Machine learning approaches were used to split the compound words. This research also aims at evaluating the quality of a word segmentation by comparing it with the segmentation of reference. Results indicated that Dictionary-based word segmentation showed better results in segmenting a compound word compared to the Machine learning segmentation when technical words were involved. Also, to improve the results for the text classification, improving the quality of the text alone is not the key

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Lotz, Max. "Depth Inclusion for Classification and Semantic Segmentation." Thesis, KTH, Robotik, perception och lärande, RPL, 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-233371.

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The majority of computer vision algorithms only use RGB images to make inferencesabout the state of the world. With the increasing availability of RGB-D cameras it is im-portant to examine ways to effectively fuse this extra modality for increased effective-ness. This paper examines how depth can be fused into CNNs to increase accuracy in thetasks of classification and semantic segmentation, as well as examining how this depthshould best be effectively encoded prior to inclusion in the network. Concatenating depthas a fourth image channel and modifying the dimension of the initial layer of a pretrainedCNN is initially examined. Creating a separate duplicate network to train depth on, andfusing both networks in later stages is shown to be an effective technique for both tasks.The results show that depth concatenation is an ineffective strategy as it clamps the ac-curacy to the lower accuracy of the two modalities, whilst late fusion can improve thetask accuracy beyond that of just the RGB trained network for both tasks. It is also foundthat methods such as HHA encoding which revolve around calculating geometric prop-erties of the depth, such as surface normals, are a superior encoding method than sim-pler colour space transformations such as HSV. This only holds true when these depthimages are normalised over the maximum depth of the dataset as opposed to the maxi-mum depth of each individual image, thus retaining geometric consistency between im-ages. The reverse holds true for simpler colour space transformations.
Majoriteten av algoritmerna för datorseende använder bara färginformation för att dra sultsatser om hur världen ser ut. Med ökande tillgänglighet av RGB-D-kameror är det viktigt att undersöka sätt att effektivt kombinera färg- med djupinformation. I denna uppsats undersöks hur djup kan kombineras med färg i CNN:er för att öka presentandan i både klassificering och semantisk segmentering, så väl som att undersöka hur djupet kodas mest effektivt före dess inkludering i nätverket. Att lägga till djupet som en fjärde färgkanal och modifiera en förtränad CNN utreds inledningsvis. Sedan studeras att istället skapa en separat kopia av nätverket för att träna djup och sedan kombinera utdata från båda nätverken. Resultatet visar att det är ineffektivt att lägga till djup som en fjärde färgkanal då nätverket begränsas av den sämsta informationen från djup och färg. Fusion från två separata nätverk med färg och djup ökar prestanda bortom det som färg och djup erbjuder separat. Resultatet visar också att metoder så som HHA-kodning, är överlägsna jämfört med enklare transformationer så som HSV. Värt att notera är att detta endast gäller då djupbilderna är normaliserade över alla bilders maxdjup och inte i varje enskild bilds för sig. Motsatsen är sann för enklare transformationer.

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Arcila, Romain. "Séquences de maillages : classification et méthodes de segmentation." Phd thesis, Université Claude Bernard - Lyon I, 2011. http://tel.archives-ouvertes.fr/tel-00653542.

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Les séquences de maillages sont de plus en plus utilisées. Cette augmentation des besoins entraîne un développement des méthodes de génération de séquences de maillages. Ces méthodes de générations peuvent produire des séquences de maillages de natures différentes. Le nombre d'applications utilisant ces séquences s'est également accru, avec par exemple la compression et le transfert de pose. Ces applications nécessitent souvent de calculer une partition de la séquence. Dans cette thèse, nous nous intéressons plus particulièrement à la segmentation en composantes rigides de séquences de maillages. Dans un premier temps, nous formalisons la notion de séquence de maillages et proposons donc une classification permettant de désigner quelles sont les propriétés attachées à un type de séquence, et ainsi de décrire précisément quel type de séquence est nécessaire pour une application donnée. Dans un second temps, nous formalisons la notion de segmentation de séquence de maillages, et présentons également l'état de l'art des méthodes de segmentation sur les séquences de maillages. Ensuite, nous proposons une première méthode de type globale pour les séquences stables de maillages, fondée sur la fusion de régions. Par la suite, nous présentons deux autres méthodes, reposant sur la classification spectrale. La première, produit un ensemble de segmentations globales, tandis que la seconde génère une segmentation globale ou une segmentation temporellement variable. Nous mettons également en place un système d'évaluation quantitative des segmentations. Enfin, nous présentons les différentes perspectives liées à la segmentation.

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Tress, Andrew. "Practical classification and segmentation of large textural images." Thesis, Heriot-Watt University, 1996. http://hdl.handle.net/10399/720.

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Shaffrey, Cian William. "Multiscale techniques for image segmentation, classification and retrieval." Thesis, University of Cambridge, 2003. https://www.repository.cam.ac.uk/handle/1810/272033.

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Kühne, Gerald. "Motion based segmentation and classification of video objects." [S.l. : s.n.], 2002. http://www.bsz-bw.de/cgi-bin/xvms.cgi?SWB10605031.

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Tóvári, Dániel. "Segmentation Based Classification of Airborne Laser Scanner Data." [S.l. : s.n.], 2006. http://digbib.ubka.uni-karlsruhe.de/volltexte/1000006285.

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Books on the topic "Classification/segmentation"

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Sithole, George. Segmentation and classification of airborne laser scanner data. Delft: Nederlandse Commissie voor Geodesie, 2005.

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Charters, Graham Castree. Segmentation and classification in automated chromosome analysis using trainable models. Manchester: University of Manchester, 1994.

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Shusharina, Nadya, Mattias P. Heinrich, and Ruobing Huang, eds. Segmentation, Classification, and Registration of Multi-modality Medical Imaging Data. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-71827-5.

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Moukadem, Ali, Djaffar Ould Abdeslam, and Alain Dieterlen. Time-Frequency Domain for Segmentation and Classification of Non-Stationary Signals. Hoboken, USA: John Wiley & Sons, Inc., 2014. http://dx.doi.org/10.1002/9781118908686.

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K, Kokula Krishna Hari, ed. An Image Segmentation and Classification for Brain Tumor Detection using Pillar K-Means Algorithm. Chennai, India: Association of Scientists, Developers and Faculties, 2016.

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Abkar, Ali Akbar. Likelihood-based segmentation and classification of remotely sensed images: A Bayesian optimization approach for combining RS and GIS. Enschede, The Netherlands: International Institute for Aerospace Survey and Earth Sciences, 1999.

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Antonacopoulos, A. Page segmentation and classification using the description of the background: A flexible and efficient approach for documents with complex and traditional layouts. Manchester: UMIST, 1995.

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Afanas'ev, Mihail, Mihail Bendikov, and Stanislav Korunov. Fundamentals of the economy of space activities. ru: INFRA-M Academic Publishing LLC., 2021. http://dx.doi.org/10.12737/1018193.

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The textbook describes in detail the classification of space goods and services, the segments and sectors of the global space market, the development prospects and the positioning of Russian enterprises in them. The methodological feature of the course consists in new approaches to the segmentation of the market and areas of space activities, identifying their deep relationships with the space industry. The practical side of the course is aimed at studying the methodology and practice of space project management, space pricing, organization of placement and execution of space government orders, and market analytics. The tutorial contains test questions for each chapter, test tasks, and a wide selection of topics for course design. The subject of the course papers is related to the specific activities of the enterprises of the space industry. Meets the requirements of the federal state educational standards of higher education of the latest generation. It is intended for third-year undergraduate and graduate students specializing in the field of training 38.03.01 and 38.04.01 "Economics" in the specialties "Economics of Space activities", "Economics of high-tech industries".

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Intelmann, Steven S. Automated, objective texture segmentation of multibeam echosounder data: Seafloor survey and substrate maps from James Island to Ozette Lake, Washington outer coast. Silver Spring, Md: U.S. Dept. of Commerce, National Oceanic and Atmospheric Administration, National Ocean Service, National Marine Sanctuary Program, 2007.

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Suri, Jasjit S., and Ayman S. El-Baz. Artificial Intelligence Strategies for Brain Tumor Diagnosis: Segmentation and Classification Strategies in MRI. Elsevier Science & Technology Books, 2023.

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Book chapters on the topic "Classification/segmentation"

1

Gale, Shawn. "Segmentation and Classification." In Encyclopedia of Clinical Neuropsychology, 3104–5. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-57111-9_9062.

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Gale, Shawn D. "Segmentation and Classification." In Encyclopedia of Clinical Neuropsychology, 1–2. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-56782-2_9062-2.

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Pietikäinen, Matti, Abdenour Hadid, Guoying Zhao, and Timo Ahonen. "Texture Classification and Segmentation." In Computational Imaging and Vision, 69–79. London: Springer London, 2011. http://dx.doi.org/10.1007/978-0-85729-748-8_4.

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Chapman, Chris, and Elea McDonnell Feit. "Segmentation: Clustering and Classification." In R for Marketing Research and Analytics, 299–338. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-14436-8_11.

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Chapman, Chris, and Elea McDonnell Feit. "Segmentation: Clustering and Classification." In Use R!, 299–340. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-14316-9_11.

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Athanasiadis, Thanos, Phivos Mylonas, Georgios Th. Papadopoulos, Vasileios Mezaris, Yannis Avrithis, Ioannis Kompatsiaris, and Michael G. Strintzis. "Knowledge-Driven Segmentation and Classification." In Multimedia Semantics, 163–81. Chichester, UK: John Wiley & Sons, Ltd, 2011. http://dx.doi.org/10.1002/9781119970231.ch10.

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He, Jia, Chang-Su Kim, and C. C. Jay Kuo. "Interactive Segmentation: Overview and Classification." In SpringerBriefs in Electrical and Computer Engineering, 7–16. Singapore: Springer Singapore, 2013. http://dx.doi.org/10.1007/978-981-4451-60-4_2.

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Hati, Avik, Rajbabu Velmurugan, Sayan Banerjee, and Subhasis Chaudhuri. "Co-segmentation Using a Classification Framework." In Image Co-segmentation, 123–49. Singapore: Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-19-8570-6_6.

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Wang, Yaping, Hongjun Jia, Pew-Thian Yap, Bo Cheng, Chong-Yaw Wee, Lei Guo, and Dinggang Shen. "Groupwise Segmentation Improves Neuroimaging Classification Accuracy." In Multimodal Brain Image Analysis, 185–93. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-33530-3_16.

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Gong, Yang Can, Michael Brady, and Styliani Petroudi. "Texture Based Mammogram Classification and Segmentation." In Digital Mammography, 616–25. Berlin, Heidelberg: Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11783237_83.

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Conference papers on the topic "Classification/segmentation"

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Nowling, Ronald J., John Bukowy, Sean D. McGarry, Andrew S. Nencka, Oliver Blasko, Jay Urbain, Allison Lowman, et al. "Classification before Segmentation: Improved U-Net Prostate Segmentation." In 2019 IEEE EMBS International Conference on Biomedical & Health Informatics (BHI). IEEE, 2019. http://dx.doi.org/10.1109/bhi.2019.8834494.

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Li, Xiaomin, Yuanyuan Wang, Yinhui Deng, and Jinhua Yu. "Cell Segmentation Using Ellipse Curve Segmentation and Classification." In 2009 First International Conference on Information Science and Engineering. IEEE, 2009. http://dx.doi.org/10.1109/icise.2009.381.

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Jun Huang, Yuan Dong, Jiqing Liu, Chengyu Dong, and Haila Wang. "Sports audio segmentation and classification." In 2009 IEEE International Conference on Network Infrastructure and Digital Content (IC-NIDC 2009). IEEE, 2009. http://dx.doi.org/10.1109/icnidc.2009.5360872.

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Khoulqi, Ichrak, and Najlae Idrissi. "Segmentation and Classification of Cervical Cancer." In 2020 IEEE 6th International Conference on Optimization and Applications (ICOA). IEEE, 2020. http://dx.doi.org/10.1109/icoa49421.2020.9094517.

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Masood, N. A., H. M. Mashali, and Abdalla S. A. Mohamed. "Color Segmentation for Skin Lesions Classification." In 2008 Cairo International Biomedical Engineering Conference (CIBEC). IEEE, 2008. http://dx.doi.org/10.1109/cibec.2008.4786059.

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Kimball, Owen, Mari Ostendorf, and Robin Rohlicek. "Recognition using classification and segmentation scoring." In the workshop. Morristown, NJ, USA: Association for Computational Linguistics, 1992. http://dx.doi.org/10.3115/1075527.1075570.

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Khoulqi, Ichrak, and Najlae Idrissi. "Breast cancer image segmentation and classification." In the 4th International Conference. New York, New York, USA: ACM Press, 2019. http://dx.doi.org/10.1145/3368756.3369039.

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Pillai, Raji R., and Sumam Mary Idicula. "Linear text segmentation using classification techniques." In the 1st Amrita ACM-W Celebration. New York, New York, USA: ACM Press, 2010. http://dx.doi.org/10.1145/1858378.1858436.

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Ciftci, Cemalettin, Emrah Ergul, and Nafiz Arica. "Scene classification using saliency based segmentation." In 2011 IEEE 19th Signal Processing and Communications Applications Conference (SIU). IEEE, 2011. http://dx.doi.org/10.1109/siu.2011.5929766.

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Lo, Pechin, and Marleen de Bruijne. "Voxel classification based airway tree segmentation." In Medical Imaging, edited by Joseph M. Reinhardt and Josien P. W. Pluim. SPIE, 2008. http://dx.doi.org/10.1117/12.772777.

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Reports on the topic "Classification/segmentation"

1

Kimball, Owen, Mari Ostendorf, and Robin Rohlicek. Recognition Using Classification and Segmentation Scoring. Fort Belvoir, VA: Defense Technical Information Center, January 1992. http://dx.doi.org/10.21236/ada457477.

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Okun, Oleg, David Doermann, and Matti Pietikainen. Page Segmentation and Zone Classification: The State of the Art. Fort Belvoir, VA: Defense Technical Information Center, November 1999. http://dx.doi.org/10.21236/ada458676.

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Lecumberry, Federico, Alvaro Pardo, and Guillermo Sapiro. Simultaneous Object Classification and Segmentation with High-Order Multiple Shape Models. Fort Belvoir, VA: Defense Technical Information Center, May 2009. http://dx.doi.org/10.21236/ada513239.

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Marroquin, Jose L., and Federico Girosi. Some Extensions of the K-Means Algorithm for Image Segmentation and Pattern Classification. Fort Belvoir, VA: Defense Technical Information Center, January 1993. http://dx.doi.org/10.21236/ada271691.

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Alhasson, Haifa F., and Shuaa S. Alharbi. New Trends in image-based Diabetic Foot Ucler Diagnosis Using Machine Learning Approaches: A Systematic Review. INPLASY - International Platform of Registered Systematic Review and Meta-analysis Protocols, November 2022. http://dx.doi.org/10.37766/inplasy2022.11.0128.

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Review question / Objective: A significant amount of research has been conducted to detect and recognize diabetic foot ulcers (DFUs) using computer vision methods, but there are still a number of challenges. DFUs detection frameworks based on machine learning/deep learning lack systematic reviews. With Machine Learning (ML) and Deep learning (DL), you can improve care for individuals at risk for DFUs, identify and synthesize evidence about its use in interventional care and management of DFUs, and suggest future research directions. Information sources: A thorough search of electronic databases such as Science Direct, PubMed (MIDLINE), arXiv.org, MDPI, Nature, Google Scholar, Scopus and Wiley Online Library was conducted to identify and select the literature for this study (January 2010-January 01, 2023). It was based on the most popular image-based diagnosis targets in DFu such as segmentation, detection and classification. Various keywords were used during the identification process, including artificial intelligence in DFu, deep learning, machine learning, ANNs, CNNs, DFu detection, DFu segmentation, DFu classification, and computer-aided diagnosis.

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Asari, Vijayan, Paheding Sidike, Binu Nair, Saibabu Arigela, Varun Santhaseelan, and Chen Cui. PR-433-133700-R01 Pipeline Right-of-Way Automated Threat Detection by Advanced Image Analysis. Chantilly, Virginia: Pipeline Research Council International, Inc. (PRCI), December 2015. http://dx.doi.org/10.55274/r0010891.

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A novel algorithmic framework for the robust detection and classification of machinery threats and other potentially harmful objects intruding onto a pipeline right-of-way (ROW) is designed from three perspectives: visibility improvement, context-based segmentation, and object recognition/classification. In the first part of the framework, an adaptive image enhancement algorithm is utilized to improve the visibility of aerial imagery to aid in threat detection. In this technique, a nonlinear transfer function is developed to enhance the processing of aerial imagery with extremely non-uniform lighting conditions. In the second part of the framework, the context-based segmentation is developed to eliminate regions from imagery that are not considered to be a threat to the pipeline. Context based segmentation makes use of a cascade of pre-trained classifiers to search for regions that are not threats. The context based segmentation algorithm accelerates threat identification and improves object detection rates. The last phase of the framework is an efficient object detection model. Efficient object detection �follows a three-stage approach which includes extraction of the local phase in the image and the use of local phase characteristics to locate machinery threats. The local phase is an image feature extraction technique which partially removes the lighting variance and preserves the edge information of the object. Multiple orientations of the same object are matched and the correct orientation is selected using feature matching by histogram of local phase in a multi-scale framework. The classifier outputs locations of threats to pipeline.�The advanced automatic image analysis system is intended to be capable of detecting construction equipment along the ROW of pipelines with a very high degree of accuracy in comparison with manual threat identification by a human analyst. �

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Cheng, Peng, James V. Krogmeier, Mark R. Bell, Joshua Li, and Guangwei Yang. Detection and Classification of Concrete Patches by Integrating GPR and Surface Imaging. Purdue University, 2021. http://dx.doi.org/10.5703/1288284317320.

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This research considers the detection, location, and classification of patches in concrete and asphalt-on-concrete pavements using data taken from ground penetrating radar (GPR) and the WayLink 3D Imaging System. In particular, the project seeks to develop a patching table for “inverted-T” patches. A number of deep neural net methods were investigated for patch detection from 3D elevation and image observation, but the success was inconclusive, partly because of a dearth of training data. Later, a method based on thresholding IRI values computed on a 12-foot window was used to localize pavement distress, particularly as seen by patch settling. This method was far more promising. In addition, algorithms were developed for segmentation of the GPR data and for classification of the ambient pavement and the locations and types of patches found in it. The results so far are promising but far from perfect, with a relatively high rate of false alarms. The two project parts were combined to produce a fused patching table. Several hundred miles of data was captured with the Waylink System to compare with a much more limited GPR dataset. The primary dataset was captured on I-74. A software application for MATLAB has been written to aid in automation of patch table creation.

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Cheng, Peng, James V. Krogmeier, Mark R. Bell, Joshua Li, and Guangwei Yang. Detection and Classification of Concrete Patches by Integrating GPR and Surface Imaging. Purdue University, 2021. http://dx.doi.org/10.5703/1288284317320.

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This research considers the detection, location, and classification of patches in concrete and asphalt-on-concrete pavements using data taken from ground penetrating radar (GPR) and the WayLink 3D Imaging System. In particular, the project seeks to develop a patching table for “inverted-T” patches. A number of deep neural net methods were investigated for patch detection from 3D elevation and image observation, but the success was inconclusive, partly because of a dearth of training data. Later, a method based on thresholding IRI values computed on a 12-foot window was used to localize pavement distress, particularly as seen by patch settling. This method was far more promising. In addition, algorithms were developed for segmentation of the GPR data and for classification of the ambient pavement and the locations and types of patches found in it. The results so far are promising but far from perfect, with a relatively high rate of false alarms. The two project parts were combined to produce a fused patching table. Several hundred miles of data was captured with the Waylink System to compare with a much more limited GPR dataset. The primary dataset was captured on I-74. A software application for MATLAB has been written to aid in automation of patch table creation.

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Hodgdon, Taylor, Anthony Fuentes, Jason Olivier, Brian Quinn, and Sally Shoop. Automated terrain classification for vehicle mobility in off-road conditions. Engineer Research and Development Center (U.S.), April 2021. http://dx.doi.org/10.21079/11681/40219.

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The U.S. Army is increasingly interested in autonomous vehicle operations, including off-road autonomous ground maneuver. Unlike on-road, off-road terrain can vary drastically, especially with the effects of seasonality. As such, vehicles operating in off-road environments need to be in-formed about the changing terrain prior to departure or en route for successful maneuver to the mission end point. The purpose of this report is to assess machine learning algorithms used on various remotely sensed datasets to see which combinations are useful for identifying different terrain. The study collected data from several types of winter conditions by using both active and passive, satellite and vehicle-based sensor platforms and both supervised and unsupervised machine learning algorithms. To classify specific terrain types, supervised algorithms must be used in tandem with large training datasets, which are time consuming to create. However, unsupervised segmentation algorithms can be used to help label the training data. More work is required gathering training data to include a wider variety of terrain types. While classification is a good first step, more detailed information about the terrain properties will be needed for off-road autonomy.

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